Investigando o Impacto de Amostras Adversárias na Detecção de Intrusões em um Sistema Ciberfísico

• Gabriel H. N. Espindola da Silva UEL
• Rodrigo Sanches Miani UFU
• Bruno Bogaz Zarpelão UEL

DOI: https://doi.org/10.5753/sbrc.2023.488

Abstract

In this paper, we investigate the impact that adversarial examples have on machine learning algorithms used to detect intrusions in cyber-physical systems. The study considers a scenario where an attacker manages to get access to data from the target that can be used to train the adversarial model. The attacker aims to generate malicious samples using Adversarial Machine Learning to mislead the machine learning model used for intrusion detection. By running FGSM (Fast Gradient Sign Method) and JSMA (Jacobian Saliency Map Attack) attacks, we observed that prior knowledge on the target algorithm architecture can lead to more critical attacks. Also, the results showed that variations in the amount of data accessed for attack preparation can have different impacts in the experimented algorithms. Finally, FGSM could create more severe attacks than JSMA, but the latter is less intrusive and possibly harder to detect.

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